Associative networks have been long used as a way to provide intelligent machines with a working memory and applied in various domains such as Natural Language Processing or customer associations analysis. While giving out numerous practical advantages, existing Games With a Purpose (GWAPs) for eliciting associative networks cannot be employed in certain domains (for example in customer associations analysis) due to the lack of profile based filtering. In this paper we ask the following research question: “Does considering agents profile information when constructing an associative network by a game with a purpose allows to extract subjective information that might have been lost otherwise?”. In order to answer this question we present the KAT (Knowledge AcquisiTion) game that extends upon the state of the art by considering agent profiling. We formalise the game, implement it and carry out a pilot study that validates the above mentioned research hypothesis.